Toward Community-Driven Big Open Brain Science: Open Big Data and Tools for Structure, Function, and Genetics

As acquiring bigger data becomes easier in experimental brain science, com-putational and statistical brain science must achieve similar advances to fullycapitalize on these data. Tackling these problems will benefit from a moreexplicit and concerted effort to work together. Specifically, brain sciencecan be further democratized by harnessing the power of community-driven tools, which both are built by and benefit from many different people with different backgroundsand expertise. This perspective can be applied across modalities and scales and enables collabora-tions across previously siloed communities.

A. S. Charles, B. Falk, N. Turner, T. D. Pereira, D. Tward, B. D. Pedigo, J. Chung, R. Burns, S. S. Ghosh, J. M. Kebschull, and others, in Annual Review of Neuroscience.
Diffeomorphic registration with intensity transformation and missing data: Application to 3D digital pathology of Alzheimer's disease

We develop methods for accurate image registration between images of different modalities, possibly in the presence of damaged or missing tissue. We use this approach to build the first 3D reconstruction of tau tangle pathology in the medial temporal lobe for an individual who died of Alzheimer's disease.

D. Tward, T. Brown, Y. Kageyama, J. Patel, Z. Hou, S. Mori, M. Albert, J. Troncoso, and M. Miller, in Frontiers in neuroscience.
An optimized protocol for iDISCO+ rat brain clearing, imaging, and analysis
A. Branch, D. Tward, J. T. Vogelstein, Z. Wu, and M. Gallagher, in bioRxiv.
3D Mapping of Serial Histology Sections with Anomalies Using a Novel Robust Deformable Registration Algorithm
D. Tward, X. Li, B. Huo, B. Lee, P. Mitra, and M. Miller, in Multimodal Brain Image Analysis and Mathematical Foundations of Computational Anatomy.
Expanding the Computational Anatomy Gateway from clinical imaging to basic neuroscience research
D. Tward, A. Kolasny, F. Khan, J. Troncoso, and M. Miller, in Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning).
EM-LDDMM for 3D to 2D registration
D. Tward, and M. Miller, in BioRxiv.
Unbiased diffeomorphic mapping of longitudinal data with simultaneous subject specific template estimation
D. Tward, M. Miller, Alzheimer’s Disease Neuroimaging Initiative, and others, in Graphs in Biomedical Image Analysis, Computational Anatomy and Imaging Genetics.
Parametric surface diffeomorphometry for low dimensional embeddings of dense segmentations and imagery
D. Tward, M. Miller, A. Trouve, and L. Younes, in IEEE transactions on pattern analysis and machine intelligence.
Morphometry of the amygdala in schizophrenia and psychotic bipolar disorder
P. B. Mahon, D. S. Lee, H. Trinh, D. Tward, M. I. Miller, L. Younes, P. E. Barta, and J. T. Ratnanather, in Schizophrenia research.
Amygdalar atrophy in symptomatic Alzheimer's disease based on diffeomorphometry: the BIOCARD cohort
M. I. Miller, L. Younes, J. T. Ratnanather, T. Brown, H. Trinh, D. S. Lee, D. Tward, P. B. Mahon, S. Mori, M. Albert, and others, in Neurobiology of aging.

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